Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians

Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System.Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to...

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Autores principales:	Anh Quynh Tran, Long Hoang Nguyen, Hao Si Anh Nguyen, Cuong Tat Nguyen, Linh Gia Vu, Melvyn Zhang, Thuc Minh Thi Vu, Son Hoang Nguyen, Bach Xuan Tran, Carl A. Latkin, Roger C. M. Ho, Cyrus S. H. Ho
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	artificial intelligence diagnosis theoretical model intention medical students Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/c3e72e88476e4955acee23559cfa5ca1
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spelling	oai:doaj.org-article:c3e72e88476e4955acee23559cfa5ca12021-12-01T06:30:04ZDeterminants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians2296-256510.3389/fpubh.2021.755644https://doaj.org/article/c3e72e88476e4955acee23559cfa5ca12021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpubh.2021.755644/fullhttps://doaj.org/toc/2296-2565Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System.Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to use an AI-based Diagnosis Support System in 211 undergraduate medical students in Vietnam. Partial least squares (PLS) structural equational modeling was employed to assess the relationship between latent constructs.Results: Effort expectancy (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (p > 0.05). Only social influence (β = 0.527, p < 0.05) was positively related to the behavioral intention.Conclusions: This study highlights positive behavioral intentions in using an AI-based diagnosis support system among prospective Vietnamese physicians, as well as the effect of social influence on this choice. The development of AI-based competent curricula should be considered when reforming medical education in Vietnam.Anh Quynh TranLong Hoang NguyenHao Si Anh NguyenCuong Tat NguyenCuong Tat NguyenLinh Gia VuLinh Gia VuMelvyn ZhangThuc Minh Thi VuSon Hoang NguyenBach Xuan TranBach Xuan TranCarl A. LatkinRoger C. M. HoRoger C. M. HoCyrus S. H. HoFrontiers Media S.A.articleartificial intelligencediagnosistheoretical modelintentionmedical studentsPublic aspects of medicineRA1-1270ENFrontiers in Public Health, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence diagnosis theoretical model intention medical students Public aspects of medicine RA1-1270
spellingShingle	artificial intelligence diagnosis theoretical model intention medical students Public aspects of medicine RA1-1270 Anh Quynh Tran Long Hoang Nguyen Hao Si Anh Nguyen Cuong Tat Nguyen Cuong Tat Nguyen Linh Gia Vu Linh Gia Vu Melvyn Zhang Thuc Minh Thi Vu Son Hoang Nguyen Bach Xuan Tran Bach Xuan Tran Carl A. Latkin Roger C. M. Ho Roger C. M. Ho Cyrus S. H. Ho Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians
description	Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System.Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to use an AI-based Diagnosis Support System in 211 undergraduate medical students in Vietnam. Partial least squares (PLS) structural equational modeling was employed to assess the relationship between latent constructs.Results: Effort expectancy (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (p > 0.05). Only social influence (β = 0.527, p < 0.05) was positively related to the behavioral intention.Conclusions: This study highlights positive behavioral intentions in using an AI-based diagnosis support system among prospective Vietnamese physicians, as well as the effect of social influence on this choice. The development of AI-based competent curricula should be considered when reforming medical education in Vietnam.
format	article
author	Anh Quynh Tran Long Hoang Nguyen Hao Si Anh Nguyen Cuong Tat Nguyen Cuong Tat Nguyen Linh Gia Vu Linh Gia Vu Melvyn Zhang Thuc Minh Thi Vu Son Hoang Nguyen Bach Xuan Tran Bach Xuan Tran Carl A. Latkin Roger C. M. Ho Roger C. M. Ho Cyrus S. H. Ho
author_facet	Anh Quynh Tran Long Hoang Nguyen Hao Si Anh Nguyen Cuong Tat Nguyen Cuong Tat Nguyen Linh Gia Vu Linh Gia Vu Melvyn Zhang Thuc Minh Thi Vu Son Hoang Nguyen Bach Xuan Tran Bach Xuan Tran Carl A. Latkin Roger C. M. Ho Roger C. M. Ho Cyrus S. H. Ho
author_sort	Anh Quynh Tran
title	Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians
title_short	Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians
title_full	Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians
title_fullStr	Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians
title_full_unstemmed	Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians
title_sort	determinants of intention to use artificial intelligence-based diagnosis support system among prospective physicians
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/c3e72e88476e4955acee23559cfa5ca1
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Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians

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